Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 2101–2125 of 2167 papers

Title	Date	Tasks	Status
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering	Dec 12, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering	Dec 2, 2016	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Visual Question Answering with Question Representation Update (QRU)	Dec 1, 2016	Question AnsweringVisual Question Answering	—Unverified
The Development of Multimodal Lexical Resources	Dec 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain	Dec 1, 2016	Question AnsweringSpeech Recognition	—Unverified
Modeling Relationships in Referential Expressions with Compositional Modular Networks	Nov 30, 2016	Visual Question Answering (VQA)	CodeCode Available
Grad-CAM: Why did you say that?	Nov 22, 2016	Image CaptioningVisual Question Answering	CodeCode Available
Zero-Shot Visual Question Answering	Nov 17, 2016	Question AnsweringRetrieval	—Unverified
Leveraging Video Descriptions to Learn Video Question Answering	Nov 12, 2016	Question AnsweringVideo Question Answering	—Unverified
Dual Attention Networks for Multimodal Reasoning and Matching	Nov 2, 2016	Collaborative InferenceImage-text matching	CodeCode Available
Proposing Plausible Answers for Open-ended Visual Question Answering	Oct 20, 2016	Graph MatchingOpen-Ended Question Answering	—Unverified
Hadamard Product for Low-rank Bilinear Pooling	Oct 14, 2016	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Open-Ended Visual Question-Answering	Oct 9, 2016	Question AnsweringSentence	CodeCode Available
Visual Question Answering: Datasets, Algorithms, and Future Challenges	Oct 5, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Tutorial on Answering Questions about Images with Deep Learning	Oct 4, 2016	Deep LearningNatural Language Understanding	CodeCode Available
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)	Sep 21, 2016	Question AnsweringSentence	—Unverified
Graph-Structured Representations for Visual Question Answering	Sep 19, 2016	Multiple-choiceQuestion Answering	—Unverified
Measuring Machine Intelligence Through Visual Question Answering	Aug 31, 2016	Image CaptioningQuestion Answering	—Unverified
Towards Transparent AI Systems: Interpreting Visual Question Answering Models	Aug 31, 2016	Question AnsweringVisual Question Answering	—Unverified
Visual Question: Predicting If a Crowd Will Agree on the Answer	Aug 29, 2016	Question Answeringvalid	—Unverified
Solving Visual Madlibs with Multiple Cues	Aug 11, 2016	Activity PredictionAttribute	—Unverified
Focused Evaluation for Image Description with Binary Forced-Choice Tasks	Aug 1, 2016	Image CaptioningImage Description	—Unverified
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description	Aug 1, 2016	Image DescriptionImage Retrieval	—Unverified
``Look, some Green Circles!'': Learning to Quantify from Images	Aug 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified
Visual Question Answering: A Survey of Methods and Datasets	Jul 20, 2016	General KnowledgeSurvey	CodeCode Available

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified